Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Antoine, Soloy"'
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 2, p 327 (2024)
This study delves into the morphodynamic changes of pebble beaches in response to storm events, employing a combination of observational and numerical approaches. This research focuses on three extreme events, meticulously examining morhological chan
Externí odkaz:
https://doaj.org/article/80297e66ac1c4b8bb3e9b0faca45d31a
Autor:
Carlos López Solano, Emma Imen Turki, Yasser Hamdi, Antoine Soloy, Stéphane Costa, Benoit Laignel, Ángel David Gutiérrez Barceló, Nizar Abcha, Delphine Jacono, Robert Lafite
Publikováno v:
Water, Vol 14, Iss 3, p 321 (2022)
This research was carried out in the framework of the Surface Water and Ocean Topography (SWOT) program of the French National Centre of Space Studies (CNES). In the context of global climate change, increases in frequency and intensity of extreme ev
Externí odkaz:
https://doaj.org/article/4861893693fc4361ae51044dfe11fda5
Publikováno v:
Remote Sensing, Vol 12, Iss 21, p 3659 (2020)
This article proposes a new methodological approach to measure and map the size of coarse clasts on a land surface from photographs. This method is based on the use of the Mask Regional Convolutional Neural Network (R-CNN) deep learning algorithm, wh
Externí odkaz:
https://doaj.org/article/c30f83bfe738410586082fca70b7bedb
While recent studies highlighted the great mobility of boulder beaches related to the impact of storm waves, numerous researches are still needed to better understand the morphodynamic of coastal boulder accumulations. This paper provides original da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f5de75ae56cbbe41c1469e5acc0bdf0
https://doi.org/10.5194/icg2022-359
https://doi.org/10.5194/icg2022-359
Autor:
Ángel David Gutiérrez Barceló, Nicolas Lecoq, Benoit Laignel, Olivier Maquaire, Stéphane Costa, Benjamin Bazin, Antoine Soloy, Imen Turki, Loïc Le Louargant, Yves Soufflet
Publikováno v:
Coastal Engineering
Coastal Engineering, 2021, 167, pp.103894. ⟨10.1016/j.coastaleng.2021.103894⟩
Coastal Engineering, 2021, 167, pp.103894. ⟨10.1016/j.coastaleng.2021.103894⟩
(IF 5.42; Q1); International audience; Coastal systems are extremely dynamic environments exposed to many hazards, making accurate and regular monitoring a major challenge, particularly in the context of global change and sea level rise. In this fram
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a59705097bb9f8538a0e55f3543ace1
https://insu.hal.science/insu-03958476/document
https://insu.hal.science/insu-03958476/document
Autor:
Antoine, Soloy, Edoardo, Grottoli, Mark, Lorang, Bob de Graffenried, Ivan, Pascal, Bertoni, Duccio, Imen, Turki, Nicolas, Lecoq, Derek, Jackson, Emilia Guisado Pintado, Christophe, Ancey, Arthur, Trembanis, Benoit, Laignel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3728::7f393e87fd1f9fb545ade9445cadecb9
http://hdl.handle.net/11568/1122047
http://hdl.handle.net/11568/1122047
Publikováno v:
Marine Geology. 447:106796
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::73995bd75cc3385337f3034dc5e1fb93
https://doi.org/10.1016/j.margeo.2022.106796
https://doi.org/10.1016/j.margeo.2022.106796
Publikováno v:
Estuarine, Coastal and Shelf Science. 265:107694
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb32e2979eb97b8f4005cf350f86b034
https://doi.org/10.1016/j.ecss.2021.107694
https://doi.org/10.1016/j.ecss.2021.107694
Publikováno v:
Remote Sensing; Volume 12; Issue 21; Pages: 3659
Remote Sensing
Remote Sensing, MDPI, 2020, 12 (21), pp.3659. ⟨10.3390/rs12213659⟩
Remote Sensing, MDPI, 2020, 12, ⟨10.3390/rs12213659⟩
Remote Sensing, Vol 12, Iss 3659, p 3659 (2020)
Remote Sensing
Remote Sensing, MDPI, 2020, 12 (21), pp.3659. ⟨10.3390/rs12213659⟩
Remote Sensing, MDPI, 2020, 12, ⟨10.3390/rs12213659⟩
Remote Sensing, Vol 12, Iss 3659, p 3659 (2020)
This article proposes a new methodological approach to measure and map the size of coarse clasts on a land surface from photographs. This method is based on the use of the Mask Regional Convolutional Neural Network (R-CNN) deep learning algorithm, wh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f3a51da5def9187ec01389408d8ee7b
https://doi.org/10.3390/rs12213659
https://doi.org/10.3390/rs12213659